Abstract

With multiple energy sources, diverse energy demands, and heterogeneous socioeconomic factors, energy systems are becoming more and more complex and multifaceted. Therefore, it is also becoming more and more challenging to improve the efficiency, security and sustainability for such complicated systems. To address these challenges, we propose a general optimal dispatch model for integrated energy systems. Two interesting and challenging decisions of any such model is how it takes power curves of equipment into account and how it deals with nonlinearity. We use a Gaussian Process (GP) to estimate the dynamic power curves, and then we linearize the nonlinear program using special ordered sets of type 2 and solve it using CPLEX. To demonstrate the practicality of the proposed approach, we combine real world and simulated scenarios to perform extensive experiments.

Abstract

To better account the contribution of various industries around the world to embodied carbon emissions, this study proposes a new accounting method from a global perspective. Unlike traditional accounting methods, our proposed accounting method considers all industries in a country as a whole. The embodied carbon emissions of the country are first accounted for and distributed to each sector within the country according to the contribution of each industry to the country’s inherent carbon emissions. To achieve this goal, we propose a network model and algorithm for the global embodied carbon network. Based on our proposed models and algorithms, we recalculated the contribution of inherent carbon emissions in various industries. Unlike traditional accounting results, we find that supportive industries such as education, health care, and public services contribute a lot to embodied carbon emissions. Not just embodied carbon-related research, our accounting methods, and NoN models provide a model basis for other research, such as international trade and global input and output.

Abstract

Basedon the detailed mechanismmodel of the methane combustion, the kinetic parameters of the global reaction, such as pre-exponential factors and activation energy, are modeled and solved. The variations of concentration of the radicals and components over time are shown for certain mechanism models, and that for global models as well. The algebraic relationship deduced from elementary reactions to global reactions are also discussed. By the method proposed, the dynamic parameters of the global reaction from essential mechanism model were drawn and compared with those from literature. Correspondingly, this paper proposed an approach to achieve the kinetic parameters of certain global reaction, in application, from availablemechanismmodels.

Abstract

Rapid development of electric vehicles (EVs) has a negative impact on the operating and planning of the power systems, and it is important to accurately simulate the EV charging load when large amount of EV charging demands are connected to the grid. In this paper, an EV charging load simulation method is proposed based on the users’ daily travel mode. Different from most of the previous works, this paper adds additional consideration of the charging preference, location type, day type and power consumption rate to improve the simulation accuracy. First, probabilistic distribution models for many defined spatialtemporal variables are established under refined conditions to improve the modelling accuracy, and the models include Burr Type XII, lognormal, generalized extreme, Weibull and Stable distribution, etc. Second, considering additional influential factors, daily profile of EV charging load is simulated based on the established distribution models and Monte Carlo algorithm. To take the public data from the US National Household Travel Survey (NHTS) as example, the proposed method is validated. The results show that the proposed method can provide more realistic daily curve of the charging load with no requirements on the historical charging data. And, the consideration of refined modelling conditions and additional influential factors can improve the accuracy of distribution models and the load simulation.

Abstract

Methane is a short-lived climate pollutant responsible for approximately 20% of anthropogenic greenhouse gas emissions. Reducing methane emissions from the oil and gas (O&G) industry is considered among the most urgent and actionable measures to mitigate climate change. Recent reports suggest a large fraction of upstream O&G methane emissions result from a small number of super-emitter facilities, emphasizing the value of novel methods that inspect O&G facilities with greater frequency than is practical using existing techniques. Here we described an optimized method wherein O&G facilities are inspected for emissions at high frequency and high sensitivity using active laser (LiDAR) sensors mounted to aircraft. The method relies on a hierarchical clustering and routing procedure to establish optimal routes to be flown by aircraft departing from local airports and equipped with LiDAR methane sensors. Routes were optimized to inspect all well sites subject to emissions regulation in three O&G intensive regions: the Permian basin, the state of Colorado, and the state of Pennsylvania. While some cost estimates require additional field data, these modeling results suggest the optimized inspections can be performed with comparable effectiveness and up to a factor of six lower cost per inspection compared to current detection methods. This modeling exercise suggests that optimized routing may enable frequent inspection of upstream O&G facilities at large scale and potentially lead to a significant decrease in both anthropogenic methane emissions and compliance costs borne by industry.

Abstract

Policy makers are tasked with selecting, designing, and implementing policies to support the transition to a sustainable power system. As part of the task they often turn to models to quantify and compare the options available to them. In this work we investigate the importance of representing a wide range of economic and physical sources of uncertainty in the modelling used to evaluate different decarbonization policies. We investigate six different energy policies across three different methods for incorporating uncertainty into decision making models in a Portugal based case study. We find that the method for incorporating uncertainty into the model used to evaluate policies leads to differences in the resulting capacity expansion plan, in the ability to meet carbon intensity targets, and in the abatement costs of policies. Policies designed in a deterministic way can result in significant violations of the emission target and the expected costs be more than double those estimated in a deterministic way. We also find that the six policies appear roughly equivalent when analysed by the deterministic model but perform very differently when uncertainty is considered potentially biasing decision makers that ignore uncertainty. Finally, we demonstrate that the simplified inclusion of uncertainty, such as scenario analysis, often underestimates the carbon reduction effect of policies and can over or under estimate costs.

Abstract

The grid is getting complex with the addition of DERs and storage at the edge. The edge management needs to be easy and intuitive. At EQuota, we develop an analytics-driven and smart-edge-device-enabled digital solution. Compared with current technologies or practices, our solution is able to sense better, think smarter and act with ease to allow high level of scalability by Plug & Play IoT devices and analytics-enabled operation flexibility, operation optimization accommodating constraints, and market dynamics.

Abstract

This work presents a non-equilibrium kinetic model to characterize foamy oil and gas/oil two-phase flow in heavy oil and propane system from pressure depletion tests. Good agreement between experiments data and simulation results are obtained in terms of production data as well as pressure distribution. The following parameters are tuned in the history match process, including k values, gas-liquid relative permeability curves, and reaction frequency factors. The simulation results suggest that bubbles pass through pore throat smoothly and have low dissolve rate in oil phase at low pressure drop rate, which results in high gas recovery factor and low oil recovery factor. Gas bubbles expand to a larger size and block the pore throat when increasing pressure drop rate to intermediate pressure depletion rate. At this range of pressure drop rate, foamy oil and gas/oil flow characterization is influenced by both gas bubbles evolve and dissolve process, which results in low gas recovery and high oil recovery. Continue to increase the pressure drop rate could cause gas bubbles to evolve faster than dissolve back and shorten production period, which results in a relatively low gas recovery as well as low oil recovery. The simulation work presented in this paper successfully characterized foamy oil behavior in the porous media for heavy oil/propane system. The innovative methodology presented in this work could be used as a general method to characterize foamy oil flow in heavy oil/propane system.

Abstract

This paper presents an innovative hybrid and compact organic Rankine cycle (ORC) system for micro combined heat and power utilizing solar thermal energy and natural gas. The ORC investigated in this work consists of heat sources, an evaporator, a scroll expander, a condenser, a fluid pump, a recuperator, a control unit and solar heater simulator. The experimental ORC unit has an electric power capacity of 1100 We. Its advantages include short start-up time, clean and automatic operation, low maintenance and very low noise level. It is well suited for hybrid solar and natural gas energy systems. A mathematical model was established to assess the thermodynamic performance of the ORC system under various conditions. Power output and thermal efficiency were calculated using the established model.

Abstract

Refrigeration is a new field for wave rotor technology in recent years. Unsteady condensation in the wave rotor, which could affect the performance of the machine, is still a challenge. In this paper, the movement of condensed droplets in the wave rotor is investigated first and the existence of evaporation, which is responsible for the low efficiency of the wave rotor, is confirmed. Then characteristics of the flow field in the wave rotor involving condensation are obtained by numerical simulation using a condensation model that describes both nucleation and growth of droplets and an evaporation model that computes growth of droplets reversely. Finally, two important operating parameters, pressure and relative humidity of high-pressure inlet, are investigated to find out their influences towards the refrigeration performance under different condensation conditions by combining experiment results and numerical simulation. The results of this paper could help better understand unsteady condensation in wave rotor and improve design accuracy significantly.